Physiological Motion Phase Classification via Feature Space Clustering
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Solution Overview
Problem
Current systems for classifying physiological motions in medical images, such as breathing phases, are time-consuming and resource-intensive, relying on complex algorithms that consume significant computational resources and are prone to failures during real-time applications like radiotherapy.
Innovation Solution
The implementation of a clustering approach in a lower-dimensional feature space using Principal Component Analysis (PCA) and neural network autoencoders to classify motion phases efficiently, reducing computational requirements and enabling faster image labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex algorithms are used for classifying physiological motions, then classification accuracy is improved, but computational resource consumption increases and real-time application becomes difficult
Solution Approach 1:
The patent segments the complex classification task into multiple stages: (1) extracting physiological motion features from medical images, (2) reducing dimensionality using PCA to transform high-dimensional features into lower-dimensional space, and (3) performing clustering classification on the reduced features. This segmentation allows maintaining classification accuracy while significantly reducing computational resource consumption during real-time execution.
Solution Approach 2:
The patent performs preliminary dimensionality reduction using PCA before the actual classification process. By pre-processing the high-dimensional physiological motion features into a lower-dimensional feature space, the system prepares optimized input data that maintains essential classification information while reducing the computational burden of subsequent clustering operations, enabling real-time application.
2Manufacturing precision
If traditional offline treatment planning is used, then treatment plan quality is improved, but treatment delivery time is extended
Solution Approach 1:
The patent transforms the static offline treatment planning process into a dynamic real-time system by implementing fast physiological motion classification that can be executed during treatment delivery. The reduced computational complexity enables the system to adaptively classify motion phases and adjust treatment parameters in real-time, maintaining high treatment plan quality while dramatically reducing the time loss between planning and delivery.
3Measurement precision
If high-dimensional feature representation is used, then classification accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent applies Principal Component Analysis to transform the physiological motion features from high-dimensional space into a lower-dimensional feature space. This dimensionality change preserves the essential variance and classification information in the data while significantly reducing the number of features that need to be processed, thereby increasing processing speed without substantially compromising classification accuracy.
Data Source
AI summary
Systems and methods are disclosed for performing operations comprising: receiving a plurality of training images representing different phases of a periodic motion of a target region in a patient; applying a model to the plurality of training images to generate a lower-dimensional feature space representation of the plurality of training images; clustering the lower-dimensional feature space representation of the plurality of training images into a plurality of groups corresponding to the different phases of the periodic motion; and classifying a motion phase associated with a new image of the target region in the patient based on the plurality of groups of the clustered lower-dimensional feature space representation of the plurality of training images.